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| Active and reactive power coordinated optimal voltage control of a distribution network based on counterfactual multi-agent reinforcement learning |
| DOI：10.19783/j.cnki.pspc.231477 |
| Key Words:distribution network active and reactive power coordinated optimization multi-agent deep reinforcement learning distributed generator |
| Author Name | Affiliation | | ZHANG Zixiao1 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China | | CUI Mingjian1 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China | | ZHANG Chengbin1 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China | | ZHANG Jian2 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China | | CAI Muliang3 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China | | ZHOU Qiukuan3 | 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Electrical
and Automation Engineering, Hefei University of Technology, Hefei 230009, China
3. State Grid
Jiangxi Electric Power Research Institute, Nanchang 330096, China |
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| Abstract:The integration of a significant number of distributed generators has altered the structure and control methods in distribution networks. To address the voltage stability issues caused by the intermittency and fluctuation of distributed generators, this paper proposes the stabilization of the distribution network voltage by adjusting the distribution of reactive and active power flows within the system. A distribution network voltage coordinated optimization method is proposed based on the counterfactual multi-agent policy gradients (COMA) algorithm. The proposed method can use a counterfactual baseline to resolve the “credit assignment” challenge in multi-agent reinforcement learning, enabling the joint optimization scheduling of active power generation and reactive power compensation devices. Agents select actions based on local observations, thereby reducing the system’s communication load and eliminating the dependency on precise flow models, to achieve real-time optimization control of distribution networks. The feasibility and effectiveness of the proposed algorithm are demonstrated by using the improved IEEE33-node system and 141-node system. Compared with the classic control algorithms, the proposed method has further performance advantages in the voltage optimization and control problems for distribution networks. |
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